Method and system for monitoring temperature anomaly of pollution discharge outlet

Abstract

The embodiment of the application provides a kind of sewage outlet temperature anomaly monitoring method and system, can solve the problems such as traditional monitoring means, such as artificial inspection, fixed monitoring station, because of narrow coverage, poor real-time, high labor cost, and low detection accuracy.The method comprises: sewage outlet area image is collected by the infrared camera carried by unmanned aerial vehicle, to obtain the radiation brightness indicated by at least three long-wave infrared bands based on the sewage outlet area image;Based on the obtained radiation brightness indicated by at least three long-wave infrared bands, the corresponding real radiation brightness is calculated respectively;The statistical relationship between radiation ratio and temperature is established by constructing temperature inversion model through the real radiation brightness of at least three long-wave infrared bands, wherein the temperature inversion model is determined by the inter-band radiation ratio of the at least three long-wave infrared bands.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of computer, in particular to a pollution outlet temperature anomaly monitoring method and system. BACKGROUND

[0002] In the ecological environment protection work, the pollution outlet monitoring is a key link of water pollution prevention and control, which has been restricted by traditional monitoring methods for a long time. Artificial inspection needs staff to visit each pollution outlet on site, which not only consumes a lot of time and manpower, but also has a very limited monitoring range, resulting in the existence of many monitoring blind spots. At the same time, when working in a dangerous environment, the personnel safety risk is high. As an important feature of water pollution, the temperature anomaly of industrial pollution outlet is difficult to realize all-weather dynamic supervision due to the narrow coverage, poor real-time performance and high labor cost of traditional monitoring methods such as artificial inspection and fixed monitoring station.

[0003] Specifically, the traditional pollution outlet monitoring faces four major pain points: first, the coverage ability is limited, artificial inspection is restricted by geographical conditions and is difficult to cover complex water areas, and fixed monitoring station can only obtain single-point data and cannot realize dynamic tracking of the area; second, the real-time performance is seriously insufficient, the traditional periodic sampling mode leads to late discovery of anomalies, such as a chemical industry park where high-temperature wastewater leakage was not detected until 72 hours after the artificial inspection cycle was too long; third, the automation degree is low, the existing monitoring equipment needs to be manually controlled and cannot realize all-weather autonomous operation; fourth, the environmental adaptability is weak, the traditional equipment is prone to failure in extreme temperature environment, and the detection accuracy decreases significantly at night and in bad weather. SUMMARY

[0004] The pollution outlet temperature anomaly monitoring method and system provided by the embodiments of the present application can solve the problems of traditional monitoring methods such as artificial inspection, fixed monitoring station, narrow coverage, poor real-time performance, high labor cost and low detection accuracy.

[0005] The first aspect of the embodiments of the present application provides a pollution outlet temperature anomaly monitoring method, comprising:

[0006] An infrared camera carried by a drone collects images of a pollution outlet area to obtain radiation brightness indicated by at least three long-wave infrared bands based on the images of the pollution outlet area;

[0007] The obtained radiation brightness indicated by the at least three long-wave infrared bands is used to calculate the corresponding true radiation brightness respectively;

[0008] A temperature inversion model is constructed by the obtained true radiation brightness of the at least three long-wave infrared bands to establish a statistical relationship between radiation ratio and temperature, wherein the temperature inversion model is determined by the inter-band radiation ratio of the at least three long-wave infrared bands.

[0009] Optionally, the establishing the statistical relationship between the radiation ratio and the temperature comprises:

[0010] In the case of three long-wave infrared bands, the relationship between the temperature of the pollution outlet area and the true radiation brightness of the three long-wave infrared bands is obtained as:

[0011]

[0012] wherein, is the true radiation brightness of the band i, A and C are model coefficients, and a and b are atmospheric correction coefficients.

[0013] Optionally, it further comprises:

[0014] The relationship between the digital quantization value output by the infrared sensor in the infrared camera and the calibration coefficient is established, and the original DN value corresponding to the long-wave infrared band is converted into the radiation brightness of the corresponding band for radiation calibration.

[0015] Optionally, the calculating the corresponding true radiation brightness based on the obtained radiation brightness indicated by the at least three long-wave infrared bands respectively comprises:

[0016] Obtaining the meteorological parameters of the location area to which the pollution outlet belongs;

[0017] Based on the meteorological parameters, the radiation brightness indicated by the at least three long-wave infrared bands is corrected by the MODTRAN model to obtain the corresponding true radiation brightness.

[0018] Optionally, the calculating the corresponding true radiation brightness based on the obtained radiation brightness indicated by the at least three long-wave infrared bands respectively comprises:

[0019] In the case of obtaining the visible light image of the pollution outlet area, performing material classification based on the target surface of the visible light image, querying the emissivity corresponding to the material category obtained by classification, and calculating the true radiation brightness corresponding to the radiation brightness indicated by the at least three long-wave infrared bands respectively pixel by pixel based on the influence of the emissivity of the target surface on temperature inversion;

[0020] In the case of not obtaining the visible light image of the pollution outlet area, assuming a uniform emissivity, and calculating the true radiation brightness corresponding to the radiation brightness indicated by the at least three long-wave infrared bands respectively pixel by pixel based on the influence of the uniform emissivity on temperature inversion.

[0021] Optionally, it further comprises:

[0022] Based on the geographical position information of the pollution outlet, the flight path of the unmanned aerial vehicle is planned and the point distribution design is performed, and the control points selected in the non-pollution influence area around the pollution outlet are included in the point distribution design.

[0023] Optionally, further comprising:

[0024] Based on the sample data of the control point and the abnormal pollution outlet point, the water temperature abnormal dynamic threshold of the pollution outlet is adjusted by a statistical dynamic method:

[0025] .

[0026] wherein k is a coefficient, is the mean value of the water temperature data of the control point, is the standard deviation representing the fluctuation range of the water temperature.

[0027] The second aspect of the embodiment of the present application provides a pollution outlet temperature anomaly monitoring device, comprising:

[0028] The acquisition unit is configured to acquire images of the pollution outlet area by an infrared camera carried by a UAV, so as to obtain radiation brightness indicated by at least three long-wave infrared wave bands based on the images of the pollution outlet area.

[0029] The preprocessing unit is configured to calculate corresponding real radiation brightness based on the obtained radiation brightness indicated by the at least three long-wave infrared wave bands.

[0030] The analysis unit is configured to construct a temperature inversion model by the obtained real radiation brightness of the at least three long-wave infrared wave bands, so as to establish a statistical relationship between radiation ratio and temperature, wherein the temperature inversion model is determined by the inter-band radiation ratio of the at least three long-wave infrared wave bands.

[0031] The third aspect of the embodiment of the present application provides an electronic system, comprising a memory and a processor, wherein the processor is configured to implement the steps of the above-mentioned pollution outlet temperature anomaly monitoring method when executing the computer program stored in the memory.

[0032] The fourth aspect of the embodiment of the present application provides a computer readable storage medium, which stores a computer program, wherein the computer program is executed by a processor to implement the steps of the above-mentioned pollution outlet temperature anomaly monitoring method.

[0033] In summary, the sewage outlet temperature anomaly monitoring method provided by the embodiment of the application, through the infrared camera carried by the unmanned aerial vehicle, collects the sewage outlet area image, to obtain the radiation brightness indicated by at least three long-wave infrared bands based on the sewage outlet area image; based on the obtained radiation brightness indicated by at least three long-wave infrared bands, the corresponding real radiation brightness is calculated respectively; through the obtained real radiation brightness of at least three long-wave infrared bands, a temperature inversion model is constructed to establish the statistical relationship between the radiation ratio and the temperature, wherein the temperature inversion model is determined by the inter-band radiation ratio of the at least three long-wave infrared bands. Compared with the traditional manual inspection of at most detecting ten sewage outlets in one day, the unmanned aerial vehicle can complete automatic inspection of dozens of kilometers of route per day, realizing comprehensive scanning of large areas such as rivers and lakes. The system can be set to periodic inspection to realize continuous dynamic recording of the water temperature of the sewage outlet, and timely discovery of illegal sewage behaviors such as night-time illegal discharge and sudden sewage discharge. The operation risk of manual work in high-pollution, high-temperature and high-humidity areas is avoided, and the remote control operation of the unmanned aerial vehicle greatly reduces safety accidents. The radiation brightness ratio is modeled using three infrared bands, which is equivalent to establishing a stable prediction model for temperature through the relative change rule of inter-band brightness, bypassing the high dependence of traditional absolute brightness correction and accurate modeling of emissivity. This method significantly improves the temperature inversion accuracy under complex conditions such as water surface reflection changes, humidity and cloud weather fluctuations, and sensor noise, and is a key link to realize non-contact, large-scale and automatic detection of water body high-temperature pollution, and has wide engineering adaptability and ecological governance value.

[0034] Correspondingly, the sewage outlet temperature anomaly monitoring device, the electronic system and the computer readable storage medium provided by the embodiment of the application also have the above technical effects. BRIEF DESCRIPTION OF DRAWINGS

[0035] Figure 1 A possible flowchart of a sewage outlet temperature anomaly monitoring method provided by the embodiment of the application;

[0036] Figure 2 A possible schematic structural block diagram of a sewage outlet temperature anomaly monitoring device provided by the embodiment of the application;

[0037] Figure 3 A possible hardware structure schematic diagram of a sewage outlet temperature anomaly monitoring device provided by the embodiment of the application;

[0038] Figure 4 A possible schematic structural block diagram of an electronic system provided by the embodiment of the application;

[0039] Figure 5 A possible schematic structural block diagram of a computer readable storage medium provided by the embodiment of the application. DETAILED DESCRIPTION

[0040] The embodiment of the present application provides a sewage outlet temperature anomaly monitoring method and system, which can solve the problems of narrow coverage, poor real-time performance, high labor cost and low detection accuracy of traditional monitoring methods such as manual inspection and fixed monitoring station.

[0041] The terms "first", "second", "third", "fourth" and the like in the description, claims, and drawings of the present application (if any) are used to distinguish between similar objects, not necessarily in an ordinal sense or in a particular sequence. It should be understood that the use of such terms is interchangeable under appropriate circumstances such that the embodiment described herein can operate in other sequences than those illustrated or described herein. Moreover, the terms "comprise" and "have", and any variations thereof, are intended to cover a non-exclusive inclusion, for example, a process, method, system, product, or apparatus that includes a list of steps or units not necessarily limited to those clearly listed, but can include other steps or units not clearly listed or inherent to such processes, methods, products, or apparatus. The technical solutions in the embodiments of the present application will be described in detail below with reference to the drawings of the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, not all.

[0042] Please refer to Figure 1 The flowchart of the sewage outlet temperature anomaly monitoring method provided by the embodiment of the present application can specifically include: S110-S130.

[0043] S110, the infrared camera carried by the unmanned aerial vehicle collects the sewage outlet area image, so as to obtain the radiation brightness indicated by at least three long-wave infrared bands based on the sewage outlet area image.

[0044] S120, the corresponding true radiation brightness is calculated based on the obtained radiation brightness indicated by at least three long-wave infrared bands.

[0045] S130, the temperature inversion model is constructed by the obtained true radiation brightness of at least three long-wave infrared bands, so as to establish the statistical relationship between the radiation ratio and the temperature, wherein the temperature inversion model is determined by the inter-band radiation ratio of the at least three long-wave infrared bands.

[0046] Exemplarily, a UAV platform with automatic flight capability can be configured, and a multispectral infrared camera supporting imaging of at least three different long-wave infrared bands is installed below the UAV platform. For example, sensor channels with detection wavelengths in the ranges of 8.5-11 μm, 11-11.5 μm, and 11.5-12.5 μm are selected, and synchronous acquisition is achieved through a light splitting filter or a multi-chip array structure. The UAV performs low-altitude cruising flight according to a preset path in the area of the sewage outlet, and the system automatically triggers an image acquisition command to obtain an infrared image sequence including the sewage outlet and the surrounding water body. In this way, the thermal infrared remote sensing is used to detect the thermal radiation of the ground object, and is particularly suitable for capturing thermal anomaly phenomena in the water body caused by industrial wastewater discharge. Through unmanned and non-contact operation, the labor cost and operation risk are significantly reduced, and the spatial coverage range and time density of the inspection are improved, so that automatic sensing of the thermal anomaly of the surface water body is achieved.

[0047] Exemplarily, the collected multi-band infrared image is original image data in the form of a DN (Digital Number) value, and needs to be subjected to radiation brightness inversion according to the calibration parameters of the infrared camera. This step is based on the gain and offset parameters provided by the equipment, and performs gray value conversion for each band image pixel by pixel to obtain the real radiation brightness L1, L2, and L3 of each band. This process can also combine an atmospheric correction model to compensate for interference factors such as water vapor absorption in the near-surface layer. Based on the calibration formula of the law of thermal radiation and the response function of the infrared detector, the physical reality of the radiation brightness in the infrared image data is ensured, distortion caused by the nonlinearity of the sensor and gain offset is avoided, and a high-quality data basis is established for subsequent temperature inversion.

[0048] Exemplarily, by acquiring three different waveband true radiation brightness values, the radiation ratio between the wavebands is constructed (for example, R12=L1 / L2, R23=L2 / L3), and then a mapping relationship function with the ground surface temperature is established, that is, a temperature inversion model is constructed. Traditional infrared remote sensing often relies on single waveband for blackbody fitting, which is greatly disturbed by the change of surface emissivity and atmospheric absorption, while the three-waveband radiation ratio model weakens the dependence on absolute radiation intensity by comparing the relative relationship of brightness of each waveband, thereby improving the robustness and anti-interference ability of temperature estimation. Especially for water surface reflectivity which is unstable, the use of the relative relationship between the three wavebands can effectively reduce error fluctuation, and is especially suitable for dynamic monitoring of complex environments such as pollution diffusion areas. Compared with the traditional single-waveband inversion method, the inversion model has higher precision stability, and significantly improves the spatial positioning ability and timeliness judgment ability of temperature anomaly of pollution.

[0049] Exemplarily, the system can upload the analysis results such as heat map, temperature data, abnormal coordinates, etc. to the environmental monitoring platform through wireless transmission, and generate historical time sequence records for comparative analysis and trend judgment. At the same time, the platform can be linked with the law enforcement system, and automatically push the abnormal points to the mobile terminal for the law enforcement personnel to arrive at the scene for verification at the first time. Based on the remote information processing and Internet of Things linkage mechanism, a comprehensive supervision logic from problem discovery to response disposal is constructed, which greatly improves the intelligent level and execution efficiency of environmental governance.

[0050] In summary, the above-mentioned embodiments provide a pollution outlet temperature anomaly monitoring method. An infrared camera carried by a UAV collects images of a pollution outlet area to obtain radiation brightness indicated by at least three long-wave infrared bands based on the images of the pollution outlet area. The obtained radiation brightness indicated by the at least three long-wave infrared bands is used to calculate corresponding real radiation brightness respectively. A temperature inversion model is constructed based on the obtained real radiation brightness of the at least three long-wave infrared bands to establish a statistical relationship between radiation ratio and temperature, wherein the temperature inversion model is determined by the inter-band radiation ratio of the at least three long-wave infrared bands. Compared with the traditional manual inspection, which can detect at most ten pollution outlets in one day, the UAV can complete automatic inspection of dozens of kilometers of route per day to realize comprehensive scanning of large areas such as rivers and lakes. The system can be set to perform regular inspection to realize continuous dynamic recording of the water temperature of the pollution outlet and timely discovery of illegal pollution behaviors such as night-time illegal discharge and sudden pollution discharge. The system avoids the operation risks in high-pollution, high-temperature and high-humidity areas, and the remote control of the UAV greatly reduces safety accidents. The use of three infrared bands for radiation brightness ratio modeling is equivalent to establishing a stable prediction model for temperature through the relative change rule of inter-band brightness, which bypasses the high dependence of traditional absolute brightness correction on accurate modeling of emissivity. This method significantly improves the temperature inversion accuracy under complex conditions such as water surface reflection changes, humidity and weather fluctuations, sensor noise, etc., and is a key link to realize non-contact, large-scale and automatic detection of water body high-temperature pollution, which has wide engineering adaptability and ecological governance value.

[0051] In an embodiment, the establishing a statistical relationship between radiation ratio and temperature comprises:

[0052] In the case of three long-wave infrared bands, the relationship between the temperature of the pollution outlet area and the real radiation brightness of the three long-wave infrared bands is:

[0053]

[0054] wherein, is the real radiation brightness of band i, A and C are model coefficients, and a and b are atmospheric correction coefficients.

[0055] Exemplarily, a response model of three-band infrared radiation brightness at different temperatures can be constructed based on radiation transmission theory. A large number of measured samples with calibrated temperatures can be collected by using experimental equipment or unmanned aerial vehicle field observation. Multidimensional observation samples are provided for subsequent establishment of the relationship between the band ratio and temperature, and supervised fitting of the model is realized. In infrared remote sensing, the radiation absorption, scattering and attenuation of the atmosphere (especially the near-surface layer) to different bands must be considered. The atmospheric migration parameters a, b and C of the three bands can be calculated based on the atmospheric profile information (temperature, humidity, aerosol, etc.), which are used as initial parameters of the model, can compensate for the radiation distortion of different bands on the atmospheric path, and make the subsequent ratio modeling more physically consistent and regionally adaptable. The brightness ratio and actual temperature in the collected samples can be functionally fitted by using a nonlinear regression method, the training data are fitted by adjusting the parameters A, a and b, and finally a complete inversion function is established. In this way, the complex radiation behavior is simplified to a controllable logarithmic function form, realizing stable and repeatable temperature inversion formula packaging. Supervised learning methods such as support vector machine regression (SVR) can be introduced to further enhance the nonlinear fitting process and improve the generalization ability. In addition, considering the differences in atmospheric conditions and surface emissivity in different regions, the model parameters can be fine-tuned regionally, and small-sample ground measurement verification can be performed before deployment, adjusting the global model to a locally adaptive form.

[0056] For example, by constructing a three-band radiance ratio model, the temperature of the pollution outlet and its surrounding water body can be accurately retrieved. The model takes the calibrated true radiance of the three bands as input, constructs two radiance ratio terms, such as the brightness difference ratio between band 1 and band 2, and the brightness ratio between band 3 and band 2, and establishes a logarithmic nonlinear mapping model of temperature. The temperature retrieval relationship is fitted by regression analysis and statistical learning method based on the measured water temperature samples and image brightness data, and the key modeling parameters include normalization coefficient, band correction factor and atmospheric migration correction term, etc. Compared with the traditional single-band brightness temperature method or double-color temperature retrieval method, this method significantly improves the resistance of the model to environmental interference, especially in complex scenes such as water surface reflection, partial cloud cover, and thin mist interference. Through the redundant information of three bands, a robust statistical relationship between temperature and radiance ratio is established, which makes the model have stronger resolution ability in scenes with weak temperature difference around the pollution outlet and small water body disturbance, especially suitable for identifying hidden abnormal pollution behaviors such as slow release and night-time illegal discharge. In order to further enhance the applicability of the model in different regions and different seasons, after the temperature retrieval model is constructed, a machine learning optimization process and regional calibration link are also needed. For example, through machine learning algorithms such as support vector regression (SVR), polynomial regression, and random forest, the residual of the regression model is nonlinearly fitted and learned to improve the generalization ability of the model to unknown samples. At the same time, a set of normalization parameters and emissivity compensation coefficients are set for different monitoring areas to adapt to the changing needs of different water bodies and different background reflection characteristics. This calibration process can be quickly adjusted in a small area with a small amount of ground temperature samples. It can be understood that although there is a modelable function relationship between thermal radiation and temperature, its specific form is easily affected by the ground material, atmospheric disturbance and time period change, so a learning correction mechanism can be introduced to ensure long-term operation stability. Therefore, not only can it achieve long-term high-frequency automatic inspection after deployment, but also can maintain high temperature identification accuracy in multiple geographic locations or different industrial pollution outlet environments. Finally, based on the temperature retrieval results, a heat map of the pollution outlet area is generated and temperature anomaly identification is performed. The system performs spatial sliding analysis and threshold comparison processing on the temperature map to automatically identify areas with significant temperature difference from the surrounding water body. If the temperature difference exceeds the preset threshold, such as more than 5°C higher than the average background water temperature, the area can be marked as a temperature anomaly area, and the corresponding coordinates, area, anomaly degree and other indicators are output. At the same time, the trend of the historical multi-day temperature sequence can be analyzed to identify the frequency and time period of abnormal discharge, providing evidence support for environmental law enforcement and pollution source tracing. Combined with time series image analysis and spatial temperature clustering algorithm, not only can it find sudden anomalies (such as one-time high-temperature wastewater discharge), but also can track low-intensity continuous pollution behavior, helping environmental supervision departments to establish a dynamic tracing mechanism and improve the initiative of early warning and the efficiency of law enforcement.

[0057] In an embodiment, further comprising:

[0058] The relationship between the digital quantization value of the infrared sensor output in the infrared camera and the calibration coefficient is established, and the original corresponding long-wave infrared band DN value is converted into the radiation brightness of the corresponding band for radiation calibration:

[0059] It can be understood that the radiation calibration establishes the relationship between the DN value of the infrared sensor output and the calibration coefficient through laboratory calibration, eliminates the device itself error, and converts the original DN value into the radiation brightness The infrared imaging captures the thermal radiation intensity of the ground object, and the radiation brightness of which follows the Planck radiation law, and the calculation formula between the DN value indicated by the infrared image and the calibration coefficient is:

[0060]

[0061] Wherein, k and l are camera calibration coefficients, provided by the laboratory or manufacturer.

[0062] In an embodiment, the method further comprises:

[0063] Obtaining the meteorological parameters of the location area to which the pollution outlet belongs;

[0064] Based on the meteorological parameters, the radiation brightness indicated by the at least three long-wave infrared bands is corrected by the MODTRAN model to obtain the corresponding true radiation brightness.

[0065] It can be understood that the meteorological parameters of the pollution outlet, such as atmospheric uplink radiation, sky downlink radiation, and atmospheric transmittance, are collected by the infrared thermal imaging sensor carried by the unmanned aerial vehicle in real time during flight, and input into the MODTRAN model. The model calculates the attenuation degree of the atmosphere to different wavelengths of thermal radiation through complex radiation transmission, and then obtains the corrected radiation brightness.

[0066]

[0067] Wherein, is the radiation brightness received by the camera, is the atmospheric uplink radiation, is the sky downlink radiation, is the atmospheric optical thickness, is the corrected radiation brightness.

[0068] In an embodiment, the method further comprises:

[0069] In a case where the visible light image of the pollution outlet area is acquired, material classification is performed based on a target surface of the visible light image, an emissivity corresponding to a material category obtained through the classification is queried, and real radiation brightness corresponding to radiation brightness indicated by the at least three long-wave infrared bands is calculated pixel by pixel based on an influence of the emissivity of the target surface on temperature inversion.

[0070] In a case where the visible light image of the pollution outlet area is not acquired, a uniform emissivity is assumed, and real radiation brightness corresponding to radiation brightness indicated by the at least three long-wave infrared bands is calculated pixel by pixel based on an influence of the uniform emissivity on temperature inversion.

[0071] For example, in combination with target surface characteristics and environmental factors, temperature errors caused by emissivity differences are effectively eliminated, the influence of target surface emissivity on temperature inversion is considered, the visible light image is used, if the visible light image is available, the material is classified through texture analysis or machine learning, and the emissivity database is queried. If not available, assume uniform emissivity (such as water surface

[0072]

[0073] wherein, is the surface emissivity, which can be classified through a visible light image or queried from a database, may be the atmospheric corrected radiation brightness, and the real radiation brightness is equivalent to the above .

[0074] In an embodiment, the method further comprises:

[0075] The flight path of the unmanned aerial vehicle is planned and the distribution point is designed based on the geographical position information of the pollution outlet, and the distribution point design includes a control point selected in a non-pollution affected area around the pollution outlet.

[0076] ​It can be understood that the detailed geographic information of the sewage outlet in the river monitoring area can be obtained by using a geographic information system (GIS), and a flight path of the unmanned aerial vehicle is planned by combining a path planning algorithm. The path fully covers all known and potential sewage outlet areas, while considering surrounding obstacles such as bridges and trees, and setting a safe flight distance. For example, in the area close to the bridge, the flight path automatically avoids a certain height range above the bridge to prevent the unmanned aerial vehicle from colliding. In the surrounding area of the sewage outlet, after field investigation, a clean water body area upstream of the river is selected as a control point to avoid the influence of sewage outlet pollution on temperature monitoring of the control point. According to the size of the sewage outlet and the complexity of the surrounding environment, multiple control groups of monitoring points are set. The monitoring points are evenly distributed within a certain range around the sewage outlet, forming a spatial temperature matrix. At the same time, a fixed route including the sewage outlet and all control points is designed, the flight height is set to 120 meters, and the speed is set to 6 m / s. Daily monitoring is carried out according to a frequency of 3 times a day, and when the system preliminarily detects an abnormal temperature trend, the collection period is increased to 1 time per hour. The following table shows an example of the setting of the sewage outlet points:

[0077]

[0078] For example, the infrared thermal imaging parameters of the sewage outlet and the meteorological parameters can be obtained, the thermal infrared camera (such as FLIR / Seek, etc.) is carried by the unmanned aerial vehicle, the long-wave infrared band band 1 (10.6 μm), band 2 (11.2 μm), and band 3 (12.0 μm) corresponding to the (DN value) of the thermal infrared image, the visible light image, the GPS coordinates, the IMU unmanned aerial vehicle attitude data (pitch, roll, yaw), and the meteorological parameters (temperature, humidity, air pressure, wind speed) are synchronously collected to ensure that all sensor data are synchronized through hardware or software triggering.

[0079] In one embodiment, it further comprises:

[0080] Based on the sample data of the control points and the abnormal sewage outlet points, the sewage outlet water temperature abnormal dynamic threshold is adjusted by a statistical dynamic method:

[0081] .

[0082] wherein k is a coefficient, is the mean value of the water temperature reflecting the water temperature data of the control points, is the standard deviation representing the fluctuation range of the water temperature.

[0083] It can be understood that, for the dynamic threshold calculation of the water temperature of the sewage outlet, the sample data of normal and abnormal points are combined, and the adaptive adjustment of the threshold is realized through a statistical model updated in real time or periodically. The normal point data can be selected from reference points without sewage interference or meeting the emission standard, such as the water temperature data of natural water bodies and compliant discharge outlets, reflecting the natural fluctuation law of background water temperature, such as changes affected by seasons, day and night, and climate. The abnormal point data can be selected from sewage outlets that obviously exceed the standard, such as the water temperature data of industrial wastewater direct discharge and non-compliant discharge outlets, reflecting the characteristics of abnormal water temperature, such as sharp increase / decrease in a short time.

[0084] Please refer to Figure 2 An embodiment of the sewage outlet temperature anomaly monitoring device in the present application can include:

[0085] The acquisition unit 201 is configured to acquire images of the sewage outlet area by an infrared camera carried by a UAV, so as to obtain radiation brightness indicated by at least three long-wave infrared bands based on the images of the sewage outlet area.

[0086] The preprocessing unit 202 is configured to calculate the corresponding true radiation brightness based on the obtained radiation brightness indicated by the at least three long-wave infrared bands.

[0087] The analysis unit 203 is configured to construct a temperature inversion model by the true radiation brightness of the at least three long-wave infrared bands, so as to establish a statistical relationship between the radiation ratio and the temperature, wherein the temperature inversion model is determined by the inter-band radiation ratio of the at least three long-wave infrared bands.

[0088] In summary, the above-mentioned embodiments provide a pollution outlet temperature anomaly monitoring device. An infrared camera carried by a UAV collects pollution outlet area images to obtain radiation brightness indicated by at least three long-wave infrared bands based on the pollution outlet area images. The obtained radiation brightness indicated by the at least three long-wave infrared bands is used to calculate corresponding real radiation brightness respectively. A temperature inversion model is constructed based on the obtained real radiation brightness of the at least three long-wave infrared bands to establish a statistical relationship between radiation ratio and temperature, wherein the temperature inversion model is determined by the inter-band radiation ratio of the at least three long-wave infrared bands. Compared with the traditional manual inspection of at most ten pollution outlets in one day, the UAV can complete automatic inspection of dozens of kilometers of route per day to realize comprehensive scanning of large areas such as rivers and lakes. The system can be set to perform regular inspection to realize continuous dynamic recording of the water temperature of the pollution outlet and timely discovery of illegal pollution behaviors such as night-time illegal discharge and sudden pollution. The operation risk of manual work in high-pollution, high-temperature and high-humidity areas is avoided, and the remote control operation of the UAV greatly reduces safety accidents. The radiation brightness ratio is modeled using three infrared bands, which is equivalent to establishing a stable prediction model for temperature through the relative change rule of inter-band brightness, bypassing the high dependence of traditional absolute brightness correction on accurate modeling of emissivity. This method significantly improves the temperature inversion accuracy under complex conditions such as water surface reflection changes, humidity and cloud weather fluctuations, and sensor noise, and is a key link to realize non-contact, large-scale and automatic detection of water body high-temperature pollution, which has wide engineering adaptability and ecological governance value.

[0089] The above Figure 2 The pollution outlet temperature anomaly monitoring device in the embodiments of the present application is described from the perspective of modular functional entities. The pollution outlet temperature anomaly monitoring device 300 in an embodiment of the present application is described in detail from the perspective of hardware processing. Please refer to Figure 3 The pollution outlet temperature anomaly monitoring device 300 in an embodiment of the present application includes:

[0090] The input device 301, the output device 302, the processor 303 and the memory 304, wherein the number of the processor 303 can be one or more, Figure 3 In some embodiments of the present application, the input device 301, the output device 302, the processor 303 and the memory 304 can be connected through a bus or other means, wherein, Figure 3 In some embodiments of the present application, the input device 301, the output device 302, the processor 303 and the memory 304 can be connected through a bus or other means, wherein,

[0091] The processor 303 is used to execute the following steps by calling the operation instructions stored in the memory 304:

[0092] An infrared camera carried by a UAV collects pollution outlet area images to obtain radiation brightness indicated by at least three long-wave infrared bands based on the pollution outlet area images.

[0093] calculate corresponding real radiance brightnesses respectively based on the obtained radiance brightnesses indicated by the at least three long-wave infrared wavebands;

[0094] construct a temperature inversion model by using the obtained real radiance brightnesses of the at least three long-wave infrared wavebands to establish a statistical relationship between a radiance ratio and temperature, wherein the temperature inversion model is determined by inter-band radiance ratios of the at least three long-wave infrared wavebands.

[0095] by calling the operation instructions stored in the memory 304, the processor 303 is further configured to perform Figure 1 any of the embodiments.

[0096] Please refer to Figure 4 , Figure 4 the embodiment of the electronic system provided in the embodiments of the present application.

[0097] As Figure 4 shown, the present application provides an electronic system, comprising a memory 410, a processor 420 and a computer program 411 stored in the memory 410 and executable on the processor 420, and the processor 420 implements the following steps when executing the computer program 411:

[0098] acquire radiance brightnesses indicated by at least three long-wave infrared wavebands based on the image of the sewage outlet area collected by the infrared camera carried by the unmanned aerial vehicle;

[0099] calculate corresponding real radiance brightnesses respectively based on the obtained radiance brightnesses indicated by the at least three long-wave infrared wavebands;

[0100] construct a temperature inversion model by using the obtained real radiance brightnesses of the at least three long-wave infrared wavebands to establish a statistical relationship between a radiance ratio and temperature, wherein the temperature inversion model is determined by inter-band radiance ratios of the at least three long-wave infrared wavebands.

[0101] In the specific implementation process, the processor 420 can implement Figure 1 any of the embodiments.

[0102] Since the electronic system introduced in the embodiment is the device used for implementing the pollution outlet temperature anomaly monitoring device in the embodiment of the application, based on the method introduced in the embodiment of the application, a person skilled in the art can understand the specific implementation of the electronic system in the embodiment and various changes thereof, so how the electronic system implements the method in the embodiment of the application is not described in detail here, as long as the device used by a person skilled in the art to implement the method in the embodiment of the application belongs to the scope of the application.

[0103] Please refer to Figure 5 , Figure 5 An embodiment of a computer readable storage medium provided in the embodiment of the application is shown.

[0104] As Figure 5 shown, the embodiment provides a computer readable storage medium 500, which stores a computer program 511, and the computer program 511 is executed by a processor to implement the following steps:

[0105] An infrared camera carried by a UAV collects a pollution outlet area image to obtain radiation brightness indicated by at least three long-wave infrared wave bands based on the pollution outlet area image;

[0106] Real radiation brightness corresponding to the radiation brightness indicated by the at least three long-wave infrared wave bands is calculated respectively based on the obtained radiation brightness;

[0107] A temperature inversion model is constructed based on the real radiation brightness of the at least three long-wave infrared wave bands to establish a statistical relationship between radiation ratio and temperature, and the temperature inversion model is determined by inter-band radiation ratio of the at least three long-wave infrared wave bands.

[0108] In the specific implementation process, the computer program 511 can implement Figure 1 any embodiment in the corresponding embodiment.

[0109] It should be noted that in the above embodiments, the description of each embodiment has its own emphasis, and the parts not described in detail in a certain embodiment can be referred to the related description of other embodiments.

[0110] Those skilled in the art should understand that the embodiments of the application can be provided as a method, a system, or a computer program product. Therefore, the application can be in the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the application can be in the form of a computer program product implemented on one or more computer usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer usable program code.

[0111] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more flow or blocks. Figure 1 one or more flow or blocks.

[0112] These computer program instructions can also be stored in a computer readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer readable memory produce an article of manufacture including instructions which implement the function specified in the flowchart block or blocks. Figure 1 one or more flow or blocks. Figure 1 one or more flow or blocks.

[0113] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more flow or blocks. Figure 1 one or more flow or blocks.

[0114] The embodiments of the present application also provide a computer program product, which comprises computer software instructions, when the computer software instructions are executed on a processing device, causing the processing device to perform the steps of the method as ​ the flow in the sewage outlet temperature anomaly monitoring method in the corresponding embodiments.

[0115] The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on the computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center through wired (such as coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (such as infrared, wireless, microwave, etc.) mode. The computer-readable storage medium can be any available medium that can be stored by the computer or a data storage device such as a server, data center, etc. integrated with one or more available media. The available media can be a magnetic medium (for example, a floppy disk, a hard disk, a magnetic tape), an optical medium (for example, a DVD), or a semiconductor medium (for example, a solid state disk (SSD)) and the like.

[0116] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the above-described system, device and unit can refer to the corresponding process in the foregoing method embodiments, which will not be repeated here.

[0117] In several embodiments provided in the present application, it should be understood that the disclosed system, device and method can be implemented in other ways. For example, the device embodiments described above are only schematic, for example, the division of the units is only a logical function division, and actual implementation can have another division manner, for example, a plurality of units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between the units or components shown or discussed can be indirect coupling or communication connection through some interfaces, devices or units, and can be electrical, mechanical or other forms.

[0118] The units described as separate components can or can not be physically separated, and the components shown as units can or can not be physical units, that is, they can be located in one place, or can be distributed on a plurality of network units. According to actual needs, part or all of the units can be selected to achieve the purpose of the embodiments.

[0119] In addition, each of the functional units in the various embodiments of the present application can be integrated in one processing unit, or each unit can be physically present separately, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of a software functional unit.

[0120] The integrated unit, if realized in the form of a software functional unit and sold or used as an independent product, can be stored in a computer readable storage medium. Based on such understanding, the technical solutions of the present application, essentially or in part, or all or part of the technical solutions can be embodied in the form of a software product. The computer software product is stored in a storage medium, and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods according to the various embodiments of the present application. The aforementioned storage medium includes: a U disk, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk, and various other media that can store program codes.

[0121] The above-described embodiments are merely used to illustrate the technical solutions of the present application, rather than limit the technical solutions thereof; even though the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still make modifications to the technical solutions recorded in the foregoing embodiments, or make equivalent replacements to some of the technical features thereof; and these modifications or replacements do not cause the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A method for monitoring abnormal temperature at a sewage outlet, characterized in that, The method comprises the following steps: acquiring images of the sewage outlet area by an infrared camera carried by a UAV to obtain radiation brightness indicated by at least three long-wave infrared bands based on the images of the sewage outlet area; calculating corresponding real radiation brightness respectively based on the obtained radiation brightness indicated by the at least three long-wave infrared bands; constructing a temperature inversion model based on the obtained real radiation brightness of the at least three long-wave infrared bands to establish a statistical relationship between radiation ratio and temperature, wherein the temperature inversion model is determined by the inter-band radiation ratio of the at least three long-wave infrared bands; the step of establishing the statistical relationship between radiation ratio and temperature comprises: in the case that the three long-wave infrared bands are three, the relationship between the temperature of the sewage outlet area and the real radiation brightness of the three long-wave infrared bands is obtained as follows: wherein, B(λ) = A(λ) - C(λ) + a(λ) + b(λ) (2) B(λ) is the true radiance of the waveband i, A and C are model coefficients, and a and b are atmospheric correction coefficients. calculating atmospheric migration parameters a, b and C of the three bands as initial parameters based on atmospheric profile information to compensate for radiation distortion on the atmospheric path of different bands; using a nonlinear regression method to perform function fitting on the brightness ratio and the actual temperature in the collected sample, adjusting parameters A, a, b to fit the training data, and finally establishing a complete inversion function.

2. The method of claim 1, wherein, Further comprising: establishing a relationship between the digital quantization value output by the infrared sensor in the infrared camera and the calibration coefficient, converting the original DN value corresponding to the long-wave infrared band into the radiation brightness of the corresponding band for radiation calibration.

3. The method of claim 1, wherein, the step of calculating corresponding real radiation brightness respectively based on the obtained radiation brightness indicated by the at least three long-wave infrared bands comprises: obtaining meteorological parameters of the location area to which the sewage outlet belongs; performing atmospheric correction on the radiation brightness indicated by the at least three long-wave infrared bands based on the meteorological parameters by a MODTRAN model to obtain corresponding real radiation brightness.

4. The method of claim 1, wherein, the step of calculating corresponding real radiation brightness respectively based on the obtained radiation brightness indicated by the at least three long-wave infrared bands comprises: in the case that a visible light image of the sewage outlet area is obtained, performing material classification based on the target surface of the visible light image, querying the emissivity corresponding to the material category obtained by classification, and calculating the real radiation brightness corresponding to the radiation brightness indicated by the at least three long-wave infrared bands respectively based on the influence of the emissivity of the target surface on temperature inversion pixel by pixel; in the case that the visible light image of the sewage outlet area is not obtained, assuming a uniform emissivity to calculate the real radiation brightness corresponding to the radiation brightness indicated by the at least three long-wave infrared bands respectively based on the influence of the uniform emissivity on temperature inversion pixel by pixel.

5. The method of claim 1, wherein, Further comprising: planning a flight path and designing a distribution point for the UAV based on the geographical location information of the sewage outlet, and the distribution point design includes a control point selected in a non-pollution-affected area around the sewage outlet.

6. The method of claim 5, wherein, Further comprising: adjusting the dynamic threshold value of the sewage outlet water temperature anomaly based on sample data of the control point and the abnormal sewage outlet point by a statistical dynamic method: wherein k is a coefficient, is the mean value of the water temperature data of the control point, is the standard deviation indicating the fluctuation range of the water temperature.

7. A device for monitoring temperature abnormalities at a pollution outlet, characterized by The device comprises: an acquisition unit configured to acquire images of the sewage outlet area by an infrared camera carried by a UAV to obtain radiation brightness indicated by at least three long-wave infrared bands based on the images of the sewage outlet area; A preprocessing unit is configured to calculate corresponding real radiation brightness respectively based on the obtained radiation brightness indicated by the at least three long-wave infrared wave bands; An analysis unit is configured to construct a temperature inversion model by the obtained real radiation brightness of the at least three long-wave infrared wave bands, so as to establish a statistical relationship between a radiation ratio and temperature, wherein the temperature inversion model is determined by the inter-band radiation ratio of the at least three long-wave infrared wave bands.

8. An electronic system comprising a memory, a processor, characterized in that, The processor is configured to implement the steps of the pollution outlet temperature anomaly monitoring method according to any one of claims 1 to 6 when executing the computer program stored in the memory.

9. A computer readable storage medium having stored thereon a computer program, characterized in that: The computer program is configured to implement the steps of the pollution outlet temperature anomaly monitoring method according to any one of claims 1 to 6 when executed by the processor.

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